1. Field of the Invention
The present invention relates to satellite imaging of celestial bodies and more specifically, to a method, system and programmed medium for massive geodetic block triangulation resulting in determining orthorectification coefficients for the creation of wide-ranging, highly accurate maps of the surface of celestial bodies.
2. Discussion of Related Art
Various methods are known for creating detailed, mathematically precise maps of the surface of celestial bodies. The most prevalent method is ground surveying. Ground surveying, however, requires extensive effort and time to determine the positions of points, the locations of which are not precisely known, on the ground relative to other points, known as control points, the locations of which are precisely known. To overcome this drawback, photography methods were developed. Photography alleviates the time-consuming ground survey effort to a certain extent by allowing the collection of position data for many points in a single image. The science of obtaining such position data from photographs is known as photogrammetry. Ground-based images can be used for photogrammetric measurements, but aerial photography has proven more useful and popular. Unfortunately, aerial surveying is not only expensive and time-consuming, but also produces maps of only relatively small areas of the surface of celestial bodies. For each image produced by aerial photography, several different types of errors must be corrected before the image data can be used to produce a map. These errors include:
attitude: tilt effects due to aircraft/sensor orientation relative to the surface; PA0 altitude: scale effects due to distance variations between the sensor and points on the ground; PA0 local relief: distortion due to terrain variation on the surface of the celestial body; and PA0 internal sensor characteristics: inaccuracies/distortions due to imaging sensor characteristics. PA0 the angle between the plane of the Greenwich Meridian 23 and the meridian plane 24 passing through point P measured along the plane of the equator 25 (geodetic longitude); PA0 the angle between two lines in the meridian plane 24 of point P: the semi-major axis of the celestial body and a line perpendicular to the body surface at point P (geodetic latitude); and PA0 the distance between the center of the celestial body and point P.
Additionally, aerial photography uses control points (which are points on the ground with precisely known location) to correlate the image data to the surface of the celestial body. Moreover, points common to images of neighboring areas, known as tie points, must be identified to match together adjoining images.
Because each image produced using conventional aerial photography covers only a small portion of the surface of a celestial body, it would be uneconomical to attempt to map large surfaces, such as a whole continent, ocean, or earth. The synoptic perspective of satellite technology, however, has changed the economics of mapping the surface of a celestial body. Satellites in orbit around a celestial body can view large areas at once and, depending on the characteristics of the orbit, allow imaging sensors to cover almost the entire surface of a celestial body. With the advent of non-military high-resolution imaging technology, satellites can produce highly detailed images of nearly the entire surface of a celestial body in a short period of time. NASA programs such as APOLLO and LANDSAT have successfully deployed these types of imaging systems for the Earth and the moon.
An additional advantage of present-day satellite imaging is that satellite image sensors have incorporated the capability of producing digital image data. This advance allows the automatic manipulation of image data by computers for the purpose of creating orthorectified maps. An orthorectified map is a conventional planar map on which each point is represented as if the viewer were directly above that location, looking straight down. In this way, by definition, the direction of observation is situated orthogonally (at an angle of 90.degree.) to the plane of the map itself, thus negating the distorted effects of local relief.
While some satellite imagery approximates an orthogonal orientation to the surface of a celestial body, this is not the general case and satellite image data must be corrected for the same errors as aerial photography. Conventional methods for correcting satellite image data for these defects, however, require information that may be difficult and/or expensive to acquire. For example, traditional corrections for satellite/sensor tilt require knowledge of the exact orientation and position of the sensor relative to the surface of the celestial body at the instant the image is acquired, which may not be available to an acceptable degree of accuracy.
In addition, ground control points may not be available for underdeveloped and/or sparsely populated portions of celestial bodies. Without complete and accurate sensor and control point data, the error correction problem is analytically underdetermined and alternate solution methods are required.
One alternative solution, used, for example, in the products of TRIFID Corporation, utilizes an elaborate mathematical model of the satellite sensor to determine sensor characteristics for each image. This approach estimates a sensor orientation and position for each image and uses the estimated values in the error-correction calculations. Because small errors in sensor position can result in very large variations in surface image data, a very high degree of accuracy and precision is required in both the synthesis and error-correction stages of using the sensor mathematical model. These requirements serve to concentrate the effort of this approach on constructing and refining the sensor math model. In the case of numerous satellite remote sensing systems, a rigorous sensor math model is not available, and even when available, would require access to geometrically unaltered image and ephemeris data, which is likely difficult to obtain.
Recent developments in the area of computing technology have helped make satellite photogrammetry an economically feasible solution to the problem of making accurate maps of an entire celestial body. Satellite image data is now available through international data distribution networks on an inexpensive basis from government sources. In addition, the advent of cheap, powerful computing power allows inexpensive computation of orthorectification coefficients in a reasonable amount of time using a computer.
Another approach to correcting satellite image data uses an iterative computational process to correlate adjoining images, but is unsuitable for mapping the surface of a celestial body due to computational limitations. This solution uses an iterative process to compensate for a lack of control points without requiring comprehensive image sensor data. However, this technique applies to small portions of the Earth surface, not spanning more than one Universal Transverse Mercator (UTM) zone. A UTM zone is defined as an area on the surface of a celestial body spanning six degrees in longitude and latitude. Because this approach is limited to such a small area of image coverage, it cannot be used to accurately map more than a small portion of the surface of a celestial body. Nevertheless, sophisticated software tools have been developed and refined that implement complex iterative solution algorithms such as the Levenberg-Marquardt (LM) algorithm described in Demuth and Beale, Matlab Neural Network Toolbox, The Mathworks, 1994, incorporated by reference herein. These advances make accurate map-creation for an entire celestial body an economically viable option if significant amounts of independently-generated data are not required, such as detailed sensor or control point information.
Most known methods for orthorectifying image data, however, were created for relatively low-altitude aerial photography or for concentration on specific small surface areas of interest. These methods are generally unsuited for orthorectification of very large portions of the surface of a celestial body. Because these methods cannot span multiple UTM zones, they are not practically useful for the large-scale task of mapping the entire surface of a celestial body. What is more, control points are often not available in previously unmapped areas of the surface of a celestial body--areas in which satellite mapping may provide the only economical or practical solution.
The problem solved by the present invention is described in reference to FIGS. 2 and 3. Satellite 11 orbits celestial body 13 with a geodetic coordinate system superimposed. In the geodetic coordinate system, the location of point P on the surface is described by three numerical values:
When a digital image 21 is produced by the orbiting satellite 11, the points of the image roughly correspond to points on a planar approximation 22 of the surface of the celestial body 13. FIG. 3 illustrates the projection of points in geodetic coordinates on the surface of the celestial body 31 onto the planar approximation 22 for any given map projection.
In order to produce an orthographic map, information from the satellite image must be corrected for error due to tilt, scale, elevation distortion, and sensor inaccuracies. These corrections are accomplished through orthorectification calculations well-known in the art. In order to perform such calculations, orthorectification coefficients must be created for each image. The creation of orthorectification coefficients becomes more difficult when a series of images of the surface of the celestial body must be assembled together in a complete and accurate map of the surface.
There is a need, therefore, for a method and system of satellite photogrammetry capable of producing an accurate mapping the surface of a celestial body without a large number of control points, without a sensor math model, and with the ability to span multiple UTM zones.